Interactions between man and machine as the basis of economics at every level

1/20. Your trading company is planning to implement a platform to analyze customer behavior using AI. There are questions about privacy and ethics. How do you respond to these concerns?

Collect data in accordance with regulations, but you don't tell customers how it is processed. Explain the purpose for which the data is analyzed and the benefits of doing so. Say that since the customer clicked “I accept,” everything is fine. Implement transparency: the customer knows what data is being collected, has access to the results and the ability to modify or withdraw consent.

2/20. Your company is using AI to screen job applications. How do you ensure fairness in the process?

Regularly audit the system for biased patterns. Manually check only the final shortlist. Let the AI make all decisions without review. Combine AI results with human review and inclusive criteria.

3/20. The influence of automation on labour markets is becoming more visible across industries. How should you interpret its growing role in employment?

Recognize that automation changes the nature of jobs, requiring new skill sets. Assume machines will always replace humans in routine tasks. Understand that automation redefines labor and demands a systemic shift in how we prepare the workforce. See automation simply as a threat to human workers and job security.

4/20. You've been asked to evaluate the effectiveness of a new algorithm that determines task allocation in your team. What is your approach to it?

Collect data, compare it with the team's opinions, identify patterns and make adjustments together with the tool's developers, ensuring the transparency of the process. Check whether the distribution of tasks seems “fair” based on your own observations. Assume that since it's an algorithm, surely it always works objectively and without error. Examine whether the algorithm's decisions are repeatable and in line with the actual potential of employees.

5/20. You’re part of a project team responsible for designing a new service involving predictive technologies. How will you approach building a relationship with the user?

Consider the user's needs at the testing stage, but not during design. Ignore his/her needs, because the technology “knows better” anyway. Design the system according to the algorithm's predictions, without the audience's input. Involve users from the beginning, testing different versions and constantly modifying the design according to real reception.

6/20. Your company is implementing automation of the customer service department. You are concerned that human contact will completely disappear. What approach will be the most sustainable?

Integrate automation with a human role that oversees quality and responds in situations that require empathy. Automate most processes, leaving humans only for emergencies. Create a model where technology supports people in real time, allowing you to maintain the relational nature of service and ensuring continuity of human contact. Replace all employees with chatbots to reduce costs.

7/20. In sales department, you notice that salespeople treat CRM as a control tool rather than a support tool. How do you change their attitude toward the technology?

Show the concrete benefits of automating tasks and saving time. Change the way of implementation: together with the team you match the tools to their work style, show their impact on customer relations and results. Blindly ignore their resistance and enforce reporting without talking to them. Encourage use of the system, but don't offer training or support.

8/20. Your company's management is considering implementing artificial intelligence into the HR department. You are concerned that algorithms may reinforce existing biases. How do you respond?

Allow the use of AI, but don't review the rules by which it makes decisions. Agree that it's not your problem since the technology works quickly and cheaply. Propose tests and analysis of the output to see if the algorithms reproduce stereotypes. Introduce ethical review rules and create a team responsible for auditing AI decisions from the perspective of fairness.

9/20. Your retail company is transitioning from manual inventory checks to an advanced system utilizing smart sensors and AI algorithms. While this promises real-time stock updates and improved supply chain efficiency, many employees express concern about the reliability of the technology and fear that their roles may become redundant. As the operations manager, how do you address these concerns and facilitate a smooth transition?

Redefine job roles to emphasize human oversight, critical decision-making, and handling exceptions that the AI system may not address, positioning employees as essential partners in the technology integration. Provide comprehensive training focused on interpreting and responding to the system’s outputs. Avoid discussing the new system to prevent alarming staff. Assign only tech-savvy employees to interact with the new technology.

10/20. Your cross-functional product development team has started using predictive analytics to guide decision-making. While some team members are enthusiastic, others resist relying on forecasts, preferring intuition and past experience. You’ve been asked to help improve buy-in and support better integration of data into creative workflows. What’s your plan?

Enforce decisions based solely on analytics reports. Show examples where data improved past decisions. Create collaborative exercises where team members interpret predictions together and integrate them with lived expertise. Ignore resistance and proceed with the data model.

11/20. A manufacturing client is preparing to integrate collaborative robots (cobots) into their production line. The leadership team wants rapid adoption, but workers on the floor are unclear about how their responsibilities will change. There are concerns about safety, job security, and skill fit. You’re responsible for guiding the rollout process. How do you proceed?

Install cobots immediately and let staff adjust on their own. Recommend limiting cobot use to a single repetitive task. Train workers to cooperate with cobots using basic safety routines. Design adaptive workflows where cobots support human skills, involve staff in planning, and include shared performance goals.

12/20. The HR department in your company is deploying robotic process automation (RPA) to handle administrative tasks like leave approvals and performance tracking. While this should free up time for more meaningful work, many employees are worried about surveillance and losing control over their own data. You’ve been asked to introduce the system in a way that reduces fear and builds understanding. What’s your approach?

Organize workshops explaining how it works, involve staff in process design, and ensure transparent communication from day one. Hold a general briefing to explain its function and benefits. Launch it quietly and hope no one notices. Send out a mass email announcing the change without context.

13/20. A city transport agency is considering automating key functions of its public bus network, including navigation, scheduling, and dispatch operations. The goal is to improve route efficiency, reduce delays, and minimize human error. However, many bus operators and dispatch staff worry that their expertise is being undervalued - and that automation could lead to job losses. Union representatives are requesting clarity about future roles, and the public is raising concerns about safety, especially in complex urban environments. You’ve been brought in as a consultant to help shape the strategy in a way that embraces innovation while preserving human value and trust. What do you propose?

Redesign operator roles to include real-time data management, direct communication with passengers, and rapid response to unexpected events or system errors. Retrain operators to oversee automated systems from centralized control rooms, focusing on technical monitoring and system reliability. Suggest replacing all operators with automation as a cost-saving measure, assuming full trust in the system's performance. Keep operators employed only for rare emergency interventions, minimizing their active involvement in daily operations.

14/20. A smart work scheduling system is being implemented by your company. After the first weeks of operation, there are signs that some employees feel they are being treated unfairly. You are responsible for assessing the situation and making further decisions. How do you react?

Gather feedback from employees, analyze data from the system and suggest adjustments to schedules. Involve workers in reviewing the system, organize joint tests, identify gaps and make transparent changes together with the technical team. Explain workers that mistakes are possible, but you see no reason to make changes without the approval of the IT department. Assume that the system works according to the algorithm, so there is no need to explain anything to employees.

15/20. In response to the growing integration of artificial intelligence across various industries, you are tasked with developing a vocational training program centred on data analysis. The objective is to equip learners with the skills necessary to operate effectively in AI-enhanced environments, where collaboration between human expertise and machine intelligence is paramount. Recognizing the diverse backgrounds of your students and the evolving demands of the job market, how would you structure the program to best achieve this goal?

Include case studies showing AI's impact on business decisions. Focus solely on basic spreadsheet functions. Emphasize mathematical theories and coding without context. Create simulations where learners interpret AI-generated data and make informed decisions.

16/20. As the head of training at a logistics firm, you aim to increase the adoption of Internet of Things (IoT) systems among warehouse staff. Workers are expressing confusion and resistance toward interpreting and utilizing sensor data effectively. What strategy do you employ to facilitate understanding and acceptance?

Allow workers to adapt to the new system through trial and error without formal guidance. Distribute written manuals and provide a basic orientation session. Develop an interactive training program featuring real-time data simulations, role-specific use cases, and continuous performance tracking to build competence and confidence in using the IoT systems. Conduct live demonstrations followed by Q&A sessions to address immediate concerns.

17/20. You are designing ethical standards for AI in healthcare. What should be your top priority?

Use AI mainly for administrative tasks to avoid direct involvement in patient care and reduce legal risks. Maximize data accessibility to accelerate medical research, with patient consent assumed by default. Fully automate diagnostics to reduce human error, even if explainability and liability remain unclear. Ensure that AI supports – not replaces – clinical judgement, guarantees patient privacy and adheres to transparent and accountable decision-making frameworks.

18/20. A logistics company starts using AI to optimize delivery routes and reduce fuel costs. What is the most effective approach to integrating AI into daily operations?

Limit AI usage to non-critical decisions while preserving legacy manual processes to minimize disruption. Rapidly automate all operational roles to eliminate human error, even if AI systems are not fully tested or accepted by staff. Use AI only as a fallback tool when human planners are unavailable, to avoid over-reliance on technology. Provide structured training, redesign workflows to support human-AI collaboration and establish clear accountability for AI-supported decisions.

19/20. A multinational company automates several departments, creating significant profit increases. How can it ensure socially responsible economic growth?

Gradually shift remaining jobs to temporary contracts to maintain flexibility in the age of automation. Reinvent corporate branding to emphasize innovation while maintaining existing wage and employment structures. Offer one-time bonuses to employees instead of long-term wage restructuring or reinvestment strategies. Introduce profit-sharing models, reinvest savings into employee reskilling and contribute to social welfare systems to mitigate inequality.

20/20. A nation introduces laws to regulate AI and robotics across sectors. Effective AI regulation should aim to:

Delegate AI governance primarily to industry leaders, assuming market forces will organically resolve ethical challenges. Suspend AI deployment until all long-term ethical consequences are scientifically proven and universally agreed upon. Establish a regulatory framework that encourages innovation while ensuring transparency, data protection, algorithmic accountability and protection of workers’ rights. Introduce protectionist policies that limit foreign AI technologies to preserve national industries and employment.

Your result: /100

You have achieved a Low Readiness Index. Your approach to human–machine collaboration remains at an early stage. You often rely on automation without fully integrating your own judgment or critical interpretation of data. Ethical safeguards, bias prevention, and user participation in design are often missing, which can lead to mistrust and reduced adoption. In several areas, you use technology as a replacement rather than as a partner to human skills, overlooking opportunities for shared responsibility models and transparent communication. To improve, you should follow these steps.

Steps to be taken to improve your Readiness Index:

Prioritise embedding human oversight in all tech-driven processes.
Create hybrid workflows where machines enhance but do not dictate decisions.
Involve diverse stakeholders in design and testing.
Take targeted training to build confidence in interpreting and challenging AI outputs.
Strengthen communication to clearly explain how decisions are made and build user trust.

You have achieved a Moderate Readiness Index. Many of your responses indicate an awareness of the value of hybrid human–machine models, ethical reviews, and user inclusion, but these are not consistently selected. In some cases, you implement automation decisions without adequate feedback loops or cross-departmental input, and you use data without sufficient critical analysis. To strengthen your readiness, you should follow the steps below.

Steps to be taken to improve your Readiness Index:

Make ethics and bias audits standard practice.
Involve diverse teams early in project design.
Ensure training goes beyond technical operation to include strategic use of technology.
Introduce scenario-based simulations where teams jointly interpret AI outputs, compare them with human insights, and adjust processes accordingly.

You have achieved a High Readiness Index. Your results indicate strong ability to combine human insight with advanced technologies in a balanced and ethical way. You show confidence in interpreting data critically, recognising when human judgment should guide or adjust automated outputs, and maintaining transparency in decision-making. You also demonstrate skill in engaging stakeholders, designing solutions with user needs in mind, and integrating ethics into everyday practice. The scenario choices suggest you adapt quickly to change, approach automation as a partner rather than a replacement, and can manage both the technical and human dimensions of Industry 5.0. This readiness level confirms you are well prepared to operate in environments where collaboration between people and intelligent systems is central. To refine your skills even further, you should follow these steps:

Continue testing advanced personalisation in intelligent systems.
Explore multi-user collaboration tools.
Share best practices to strengthen wider readiness.

EQF level alignment

According to your results, your current competence level can be estimated as %EQF%.

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Interactions between man and machine as the basis of economics at every level